Introduction to R 2025
Faculty: Mohamed Helmy
October 6-7, 2025
I Introduction
1 Workshop Info
Welcome to the 2025 Introduction to R Canadian Bioinformatics Workshop webpage!
1.1 Pre-work
1.2 Class Photo
1.3 Schedule
2 Meet Your Faculty
2.0.0.1 Mohamed Helmy
Principal Scientist and Adjunct Professor
Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan
Saskatoon, Saskatchewan, Canada
Mohamed is a Computational Systems Biologist and Principal Scientist leading the Bioinformatics and Systems Biology Lab (BSBL) at the Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan. He received his MSc and PhD in Computational Systems Biology from Keio University (Tokyo, Japan) and completed his postdoctoral training in bioinformatics at Kyoto University and the University of Toronto. Mohamed’s interdisciplinary research profile bridges biology, computer science, and public health.
2.0.0.2 Sylvia Li
Graduate student
Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan
Saskatoon, Saskatchewan, Canada
Sylvia is a Computer science MSc student at the University of Saskatchewan, supervised by Dr. Helmy. She holds dual BSc degrees in Bioinformatics and Computer science. Currently her work focuses on bacterial genomic data.
Data and Compute Setup
2.0.0.3 Course data downloads
Coming soon!
2.0.0.4 Compute setup
Coming soon!
II Modules
3 Module 1
3.1 Lecture
3.1.1 1A
3.1.2 1B
3.2 Lab 1A
3.2.1 Variables
Create 2 numeric variables and assign values for each
x = 10
y = 6Calculate the sum of them
total = x + y
total## [1] 16Calculate the square root of the total
sr = sqrt(total)
sr## [1] 43.2.2 Data Structures
Vector
v <- c(1,2,3,4)
v## [1] 1 2 3 4Matrix
m <- matrix(1:6, nrow = 2)
m## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6Dataframe
df <- data.frame(age=c(25,30), name=c("Mo","Tom"), group=c("A", "B"))
df## age name group
## 1 25 Mo A
## 2 30 Tom BList
lst <- list(numbers=v, info=df)
lst## $numbers
## [1] 1 2 3 4
##
## $info
## age name group
## 1 25 Mo A
## 2 30 Tom B3.3 Lab 1B
3.3.1 Install BioconductoR packages
install.packages("BiocManager")
BiocManager::install("ALL")3.3.2 View patient metadata
library(BiocManager)
library(ALL)
data(ALL)
df2 <- pData(ALL)3.3.3 Quick summary
#summary(pData(ALL)[, c("age", "sex", "BT", "relapse")])
summary(df2[, c("age", "sex", "BT", "relapse")])## age sex BT relapse
## Min. : 5.00 F :42 B2 :36 Mode :logical
## 1st Qu.:19.00 M :83 B3 :23 FALSE:35
## Median :29.00 NA's: 3 B1 :19 TRUE :65
## Mean :32.37 T2 :15 NA's :28
## 3rd Qu.:45.50 B4 :12
## Max. :58.00 T3 :10
## NA's :5 (Other):133.3.4 str() and dim() functions
dim(df2)## [1] 128 21str(df2)## 'data.frame': 128 obs. of 21 variables:
## $ cod : chr "1005" "1010" "3002" "4006" ...
## $ diagnosis : chr "5/21/1997" "3/29/2000" "6/24/1998" "7/17/1997" ...
## $ sex : Factor w/ 2 levels "F","M": 2 2 1 2 2 2 1 2 2 2 ...
## $ age : int 53 19 52 38 57 17 18 16 15 40 ...
## $ BT : Factor w/ 10 levels "B","B1","B2",..: 3 3 5 2 3 2 2 2 3 3 ...
## $ remission : Factor w/ 2 levels "CR","REF": 1 1 1 1 1 1 1 1 1 1 ...
## $ CR : chr "CR" "CR" "CR" "CR" ...
## $ date.cr : chr "8/6/1997" "6/27/2000" "8/17/1998" "9/8/1997" ...
## $ t(4;11) : logi FALSE FALSE NA TRUE FALSE FALSE ...
## $ t(9;22) : logi TRUE FALSE NA FALSE FALSE FALSE ...
## $ cyto.normal : logi FALSE FALSE NA FALSE FALSE FALSE ...
## $ citog : chr "t(9;22)" "simple alt." NA "t(4;11)" ...
## $ mol.biol : Factor w/ 6 levels "ALL1/AF4","BCR/ABL",..: 2 4 2 1 4 4 4 4 4 2 ...
## $ fusion protein: Factor w/ 3 levels "p190","p190/p210",..: 3 NA 1 NA NA NA NA NA NA 1 ...
## $ mdr : Factor w/ 2 levels "NEG","POS": 1 2 1 1 1 1 2 1 1 1 ...
## $ kinet : Factor w/ 2 levels "dyploid","hyperd.": 1 1 1 1 1 2 2 1 1 NA ...
## $ ccr : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ relapse : logi FALSE TRUE TRUE TRUE TRUE TRUE ...
## $ transplant : logi TRUE FALSE FALSE FALSE FALSE FALSE ...
## $ f.u : chr "BMT / DEATH IN CR" "REL" "REL" "REL" ...
## $ date last seen: chr NA "8/28/2000" "10/15/1999" "1/23/1998" ...3.3.5 the table() function
af <- table(df2$age)
barplot(af, main = "Age frec.")
### mean and median age
mn <- mean(df2$age) # this will return NA
md <- median(df2$age) # this will return NA
mn <- mean(df2$age, na.rm = TRUE) # this will work
md <- median(df2$age, na.rm = TRUE) # this will work3.3.6 standard deviation and variance
std <- sd(df2$age, na.rm = TRUE)
vr <- var(df2$age, na.rm = TRUE)3.3.7 Extremes
mxx <- max(df2$age, na.rm = T)
mnn <- min(df2$age, na.rm = T)3.3.8 Table and (Frequency)
age_dit <- table(df2$age)3.3.9 Quick summary
summary(df2[, c("age", "sex", "BT", "relapse")])## age sex BT relapse
## Min. : 5.00 F :42 B2 :36 Mode :logical
## 1st Qu.:19.00 M :83 B3 :23 FALSE:35
## Median :29.00 NA's: 3 B1 :19 TRUE :65
## Mean :32.37 T2 :15 NA's :28
## 3rd Qu.:45.50 B4 :12
## Max. :58.00 T3 :10
## NA's :5 (Other):13